These functions have been developed to watch and analyze EEG/Video/Accelero(3A)/Magneto(3M) data in the context of the Epimouv/Capametrim project. The project aim is to recognized epileptic movement disorders by determining regions of interests on

EEG数据下载和运动想象分类,可作为一个很好用的工具箱来分析生物医学信号。-EEG data,including motor image classfication be very useful for BCI.It can be a toolbox to analyse ECG and EEG data,includeing filtering.

用于分解提取BCI竞赛数据集，将四类想象运动的有效EEG信号分别提取到四个矩阵中。-BCI competition for decomposition to extract data sets, four types of effective motor imagery EEG signals were extracted to four matrix.

BCI_MI_CSP_DNN是一种基于matlab的运动图像脑电信号分类程序。 基于matlab深度学习工具箱编写了BCI_MI_CSP_DNN程序 本程序的原理基于CSP和DNN算法 这个程序的性能是基于BCI竞赛II数据集II 提出了一种基于深度学习的运动图像脑电信号分类方法。在预处理原始脑电图信号的基础上，采用共空间模型(CSP)方法提取脑电图特征矩阵，并将其输入深度神经网络(DNN)进行训练和分类。我们的工作在BCI Competition II Dataset III上进行了实